# Sorting

**Repository Path**: mirrors_LambdaSchool/Sorting

## Basic Information

- **Project Name**: Sorting
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-25
- **Last Updated**: 2026-07-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Sorting Algorithms

In this two day project, you will be implementing many different solutions to the same problem: sort a list of integers in ascending order. You will also be using your newfound knowledge of complexity analysis to evaluate each implementation for efficiency.

# No Googling for Code

For the sorting module, it's OK to Google for things like this:

* Pseudocode
* Descriptions of algorithms
* Videos that show how the algorithm works

But you should **NOT** search for:

* Code

If you see code, avert your eyes and hit the back button!

This isn't for anti-cheating purposes. This is because _if you copy code, you won't learn the most important part of this lesson!_ One of the things we want to practice here is to take a spec and turn it into code. You'll be expected to do this incessantly at work, and we want to practice it here. Yes, it's harder this way, but you don't get better by practicing easy stuff.

Your task is to take a simple problem (sort an list of ints) and a pre-defined plan (we give you an algorithm description) and turn that into code. These steps should sound familiar, as they are 1-3 of [the Lambda Problem Solving Framework](https://github.com/BloomInstituteOfTechnology/CS-Wiki/wiki/Lambda-Problem-Solving-Framework). Soon, you will be coming up with your own plans for more complex problems so don't cheat yourself out of valuable coding practice.

That being said, please still use the 20 minute rule :slightly_smiling_face:


# Part 1

## MVP Tasks

 - Open up the [iterative_sorting](src/iterative_sorting) directory
 - Read through the descriptions of the `bubble_sort` and `selection_sort` algorithms
 - Implement `bubble_sort` and `selection_sort` in [iterative_sorting.py](src/iterative_sorting/iterative_sorting.py)
 - Test your implementation by running `test_iterative.py`

# Part 2

## MVP Tasks

 - Open up the [recursive_sorting](src/recursive_sorting) directory
 - Read through the descriptions of the `merge_sort` algorithm
 - Implement `merge_sort` in [recursive_sorting.py](src/recursive_sorting/recursive_sorting.py)
 - Test your implementation by running `test_recursive.py`

## Stretch Goals
 - Implement all the methods in the `searching.py` file in the `searching` directory.
 - Implement the `count_sort` algorithm in the `iterative_sorting` directory.
 - Implement an in-place version of `merge_sort` that does not allocate any additional memory. In other words, the space complexity for this function should be O(1).
 - Implement the `timsort` algorithm, which is a real-world sorting algorithm. In fact, it is the sorting algorithm that is used when you run Python's built-in `sort` method.